System and method for adaptive orchestration of multi-model processing and response generation

A method for orchestrated multi-model processing and response generation, the method comprising:

• receiving multimodal input data comprising at least one of text data, audio data, video data, or gesture data;
• generating structured signals from the multimodal input data;
• distributing the structured signals to a plurality of specialized learner models;
• executing each of the plurality of specialized learner models, each comprising a small language model trained for domain-specific reasoning, to generate respective domain-specific outputs;
• executing a large language model agent configured for generalized processing to generate a generalized output;
• applying arbitration logic to resolve conflicts among the respective domain-specific outputs and the generalized output;
• generating arbitration results using the arbitration logic;
• integrating curated dataset inputs and validation feedback into the arbitration results;
• generating a synthesized response from the arbitration results, the curated dataset inputs, and the validation feedback, wherein generating the synthesized response includes:
• generating a text output as a first response modality;
• generating an audio output as a second response modality;
• generating audio directives and visual directives from the synthesized response;
• mapping tokens from the synthesized response to viseme cues and gesture cues to generate mapped directives;
• time-aligning the audio directives and the mapped directives to generate aligned directives;
• generating a digital personal display output as a third response modality using the aligned directives;
• and
• transmitting the synthesized response for presentation to a user.

A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor of a computing system, cause the computing system to:

• receive multimodal input data comprising at least one of text data, audio data, video data, or gesture data;
• generate structured signals from the multimodal input data;
• distribute the structured signals to a plurality of specialized learner models;
• execute each of the plurality of specialized learner models, each comprising a small language model trained for domain-specific reasoning, to generate respective domain-specific outputs;
• execute a large language model agent configured for generalized processing to generate a generalized output;
• apply arbitration logic to resolve conflicts among the respective domain-specific outputs and the generalized output;
• generate arbitration results using the arbitration logic;
• integrate curated dataset inputs and validation feedback into the arbitration results;
• generate a synthesized response from the arbitration results, the curated dataset inputs, and the validation feedback, wherein generating the synthesized response includes:
• generating a text output as a first response modality;
• generating an audio output as a second response modality;
• generating audio directives and visual directives from the synthesized response;
• mapping tokens from the synthesized response to viseme cues and gesture cues to generate mapped directives;
• time-aligning the audio directives and the mapped directives to generate aligned directives;
• generating a digital personal display output as a third response modality using the aligned directives;
• and
• transmit the synthesized response for presentation to a user.